Identification And Validation Of The Biomarker Associated With Platinum Chemoresistance In HGSOC Based On Scissor Algorithm

Research background:High-grade serous ovarian carcinoma(HGSOC)is the most common and aggressive histological subtype of epithelial ovarian cancer.Because HGSOC patients have no obvious symptoms in the early stages of the disease and lack effective screening,about 80% of patients are diagnosed with advanced disease.The 5-year cause-specific survival rates for stage III and stage IV HGSOC patients are only 42% and 26%,respectively.Tumor reduction combined with platinum-based chemotherapy is currently the main clinical treatment for HGSOC.Unfortunately,approximately 80%of patients will relapse within five years due to chemoresistance,although patients initially show a response to platinum-based chemotherapy.At present,there is an urgent need to discover biomarkers associated with chemoresistance so that more patients can benefit from platinum-based chemotherapy.Bulk RNA-sequencing(Bulk RNA-seq)refers to a technique for gene sequencing from bulk tissue of a sample.By sequencing bulk tissue of a large number of patients and matching their phenotypic information at the same time,it is possible to deeply understand the impact of changes in bulk tissue omics information on phenotypes.However,the gene expression level in bulk sequencing is obtained from bulk tissue rather than from a single cell,which makes the accuracy of gene expression obtained by bulk RNA-seq insufficient.Single cell RNAsequencing(sc RNA-seq)is a technique that isolates all the cells in a sample tissue and then sequencing the genes of each cell.Although it is not well used for large-scale correlation sample phenotypic information,it allows the determination of gene expression at the resolution of individual cells.What’s more importantly,sc RNA-seq can also reveal the type of cells in the tissue and the proportion of various cells,which makes it to identify biomarkers more precise and efficient.Research aims:This study aims to jointly analyze bulk RNA-seq dataset and sc RNA-seq dataset of HGSOC,integrate the respective advantages of the two sequencing datasets,and combine in vitro experiments to systematically identify biomarkers associated with chemoresistance,so as to provide important guidance for developing better treatment strategies for patients and improving their prognosis.At the same time,it also aims to provide scientific theoretical basis for revealing the new mechanism of chemoresistance of HGSOC,so as to overcome chemoresistance and develop new drugs.Research methods:Firstly,we jointly analyzed sc RNA-seq dataset and bulk RNA-seq dataset of HGSOC to identify cell types associated with chemoresistance by the scissor algorithm.Secondly,infer CNV algorithm was performed to identify malignant cells.Subsequently,we used the GEPIA2 website and KM plotter website to analyze the expression of candidate biomarkers and their relationship with patient prognosis.The functional enrichment analysis was carried out to explore the potential biological function of the candidate biomarker.Finally,we validated the candidate biomarker using in vitro experiments.Research results:We identified 8,871 malignant epithelial cells in the GSE158937 dataset,of which861 cells were associated with chemoresistance.Among these malignant epithelial cells,FBXO2 is highly expressed in cells related to chemoresistance(P<0.05).In addition,based on GSE154600 dataset,it was found that the expression of FBXO2 in epithelial cells originating from chemoresistance samples was higher than that in epithelial cells originating from chemosensitivity samples(P<0.05).Patients with high FBXO2 expression had significantly worse overall survival and progression-free survival(P<0.05).Pathways analysis suggests FBXO2 may affect chemoresistance through the PI3K-Akt signaling pathway,focal adhesion,and ECM-receptor interactions.In an in vitro experiment,the 50% maximum inhibitory concentration(IC50)of cisplatin was decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2.Research conclusions:We identified FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC through an integrated analysis of single cell RNA-seq dataset and bulk RNA-seq dataset.Our findings indicate that FBXO2 may be a promising prognostic signature and drug development target in HGSOC.